About

Background

Food security in Sub-Saharan Africa is increasingly affected by climate variability, drought, water scarcity and uncertainty in agricultural production. Maize is one of the region’s most important staple crops, yet reliable and timely information on crop development and expected yield is often limited.

Existing agricultural monitoring systems provide valuable large-scale early-warning information, but they often rely on medium-resolution satellite indicators, aggregated statistics or empirical models. These approaches can be limited in heterogeneous landscapes with small fields, variable management practices and scarce ground-reference data. YPSAfrica addresses this gap by combining high-resolution Earth Observation data with physical crop-growth modelling in a Digital Twin approach.

The project moves beyond broad vegetation anomaly detection towards a more detailed, explainable and decision-oriented description of crop development, water stress, irrigation needs and expected maize production. This is particularly relevant in regions where drought, heat stress and irregular rainfall can strongly affect agricultural productivity.

YPSAfrica also investigates the added value of innovative satellite sensors. Hyperspectral EnMAP data may improve the characterization of crop vitality, plant water content and nutrient status, while thermal data from constellr can provide direct information on land-surface temperature and vegetation water stress. Together, these data sources can strengthen agricultural monitoring and support more resilient food systems.

Key objectives

YPSAfrica aims to develop and scientifically validate a methodological framework for satellite- and model-based maize yield prediction in Sub-Saharan Africa. The project focuses on the adaptation, testing and validation of existing Earth Observation, weather and geodata processing workflows under African agricultural conditions.

A central objective is to enable hindcast and accuracy analyses of maize yield and production. Existing methods for data access, preprocessing and analysis of satellite, weather and geospatial data will be adapted to the specific conditions of African agricultural systems and tested for their suitability in multi-year analyses.

The project will also advance in-season crop classification. The goal is to identify maize-growing areas robustly and as early as possible during the ongoing season. This is particularly important in regions with heterogeneous cropping systems, smallholder structures, variable management practices and limited reference data.

Another scientific and technical objective is the phenological parametrization of crop-growth simulations using satellite information. YPSAfrica will investigate how crop development stages, seasonal dynamics and maize stress signatures can be derived from Earth Observation data and used for model-based descriptions of crop development. This includes the challenge of representing variable sowing dates, heterogeneous production systems and scarce validation data in a scientifically robust way.

YPSAfrica also aims to design a user-oriented service prototype in close cooperation with African partners. Requirements, use cases, relevant indicators and evaluation criteria will be systematically collected and translated into scientific and technical specifications. The involvement of African partners supports an iterative assessment of whether the planned methods, information products and processing chains are suitable, useful and operationally relevant under local agricultural, institutional and data conditions.

A further objective is to assess the added value of innovative Earth Observation data in pilot regions in Kenya, Zambia and South Africa. The project will evaluate how hyperspectral EnMAP data can improve the detection of crop condition, crop vitality and stress-related changes. In parallel, high-resolution thermal data from constellr will be assessed for monitoring water stress, irrigation activity and water-use efficiency.

A key focus is the use of land-surface temperature data as a physical indicator of the vegetation energy and water balance. Unlike purely optical satellite methods, which mainly capture structural and biochemical plant properties, land-surface temperature enables more direct observation of transpiration processes and supports the derivation of water-stress indicators.

Together, these objectives support the experimental proof of the suitability, integration potential and added value of new satellite data sources for future operational agricultural monitoring and yield-forecasting services in Africa.

Pilot-user Involvement

Pilot users are planned because YPSAfrica is not intended as a purely technical demonstrator. The service must be shaped around real institutional workflows, local data realities and decision needs. Early pilot-user involvement ensures that the prototype is relevant, understandable and usable for the organizations that may later apply it operationally.

Pilot users help define priority use cases, indicators, spatial scales, output formats and reporting needs.

Local partners contribute knowledge, available reference data and feedback on whether the maps, indicators and yield estimates are plausible.

Workshops test how results can support food-security monitoring, agricultural planning, irrigation decisions and climate-risk assessment.

The process clarifies how African partners can access, interpret and potentially operate the service under appropriate governance arrangements.

The involvement is planned around two workshops: an initial digital workshop early in the project to collect requirements and align expectations, and an on-site workshop with the partner organization in each focus country to review results and discuss practical use towards the end of the project. Pilot users are expected to provide available data, contextual information and structured feedback. In return, they receive project results, access to the generated information products and the opportunity to influence a future operational YPSAfrica service.